Back

Predicting Biological Age Using an Accumulated Neurotoxicity Biomarker for Amyloid Beta Oligomers

Kuznetsov, A. V.

2025-03-06 biophysics
10.1101/2025.02.28.640920 bioRxiv
Show abstract

This study proposes using accumulated neurotoxicity, defined as the time integral of A{beta} oligomer concentration, as a biomarker for neuronal aging. A relationship between biological age and accumulated neurotoxicity is proposed. Numerical analysis guided the development of a new analytical solution linking the biological and calendar ages of neurons. The effects of A{beta} monomer and oligomer half-lives--key indicators of proteolytic efficiency--on biological age are examined. Both constant and age-dependent (exponentially increasing) half-life scenarios are considered. The findings indicate that increasing the half-life of A{beta} monomers and oligomers with age accelerates biological aging. Reducing A{beta} monomer production is shown to slow biological aging, with a linear relationship established between these two quantities. Additionally, biological age is found to depend linearly on the half-deposition time of A{beta} oligomers into senile plaques. The model demonstrates that biological age is irreversible, providing a theoretical explanation for why plaque-clearing therapies cannot reverse established cognitive impairment. The model also demonstrates that biological age is path-dependent rather than state-dependent.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

1
Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences
15 papers in training set
Top 0.1%
19.1%
2
Journal of Theoretical Biology
144 papers in training set
Top 0.1%
10.3%
3
PLOS ONE
4510 papers in training set
Top 20%
9.3%
4
Scientific Reports
3102 papers in training set
Top 16%
6.5%
5
PLOS Computational Biology
1633 papers in training set
Top 7%
5.0%
50% of probability mass above
6
Chaos, Solitons & Fractals
32 papers in training set
Top 0.4%
4.4%
7
Journal of Biomechanical Engineering
17 papers in training set
Top 0.1%
3.7%
8
Physical Biology
43 papers in training set
Top 0.4%
3.7%
9
The European Physical Journal Plus
13 papers in training set
Top 0.3%
2.8%
10
International Journal for Numerical Methods in Biomedical Engineering
12 papers in training set
Top 0.1%
2.1%
11
Biophysical Journal
545 papers in training set
Top 2%
2.1%
12
Computers in Biology and Medicine
120 papers in training set
Top 2%
1.7%
13
The Journal of Physical Chemistry B
158 papers in training set
Top 1%
1.4%
14
Bulletin of Mathematical Biology
84 papers in training set
Top 1%
1.4%
15
Computational and Structural Biotechnology Journal
216 papers in training set
Top 6%
1.3%
16
Cognitive Neurodynamics
15 papers in training set
Top 0.3%
1.1%
17
Biomechanics and Modeling in Mechanobiology
25 papers in training set
Top 0.6%
1.0%
18
International Journal of Molecular Sciences
453 papers in training set
Top 12%
1.0%
19
Journal of The Royal Society Interface
189 papers in training set
Top 4%
0.9%
20
Mathematical Biosciences
42 papers in training set
Top 0.9%
0.9%
21
Frontiers in Computational Neuroscience
53 papers in training set
Top 2%
0.7%
22
Cancers
200 papers in training set
Top 5%
0.7%
23
Life
27 papers in training set
Top 0.6%
0.7%
24
Cells
232 papers in training set
Top 9%
0.5%
25
ACS Chemical Neuroscience
60 papers in training set
Top 3%
0.5%